1. Technical Field of the Invention
The present invention relates to semiconductor manufacturing equipment, which includes equipment and a sensor monitoring statuses of various portions at time of equipment operation, including equipment which performs process processing by acquiring a map permitting quantitative judgment of waveform similarity based on compared values (matrix) between processing by use of equipment monitor signal data (waveforms) during a plurality of wafer processing, and which monitors the semiconductor manufacturing equipment.
The invention more specifically relates to a function of quantifying difference between signals even under the presence of signals which cannot have correlation between the signals and which have, as signal change during processing, not only change such as ramp and drift but also change including noise magnitude, variation such as hunting, and further shift (offset of signal value) and a step.
2. Description of Related Arts
A large scale integrated circuit (LSI) is formed by using many kinds of semiconductor manufacturing equipment by forming on a silicon (Si) wafer devices composed of, for example, a gate electrode and repeating dielectric film deposition and wiring formation. For the purpose of achieving higher LSI performance, higher function, and productivity improvement, minituarization of devices and circuits have been advanced, and according to ITRS (International Technology Roadmap for Semiconductor), a minimum line width of a gate electrode has become 45 nm in 2010. In addition, a manufacturing method has become more complicated. Accordingly, machining accuracy of various kinds of process equipment has improved, and further multiple function addition/informatization, for example, sensor addition and inclusion of a function of accumulating equipment data at short sampling intervals during processing have been advanced.
In an LSI wafer production line, while manufacturing condition has been optimized in order to ensure machining accuracy, efforts to prevent production volume reduction by way of equipment maintenance and problem measures have been advanced. According to, for example, International SEMATECH Manufacturing Initiative, ISMI Predictive Preventive Maintenance Implementation Guideline, Technology Transfer #10105119A-TR, described is that in order to realize Condition-based Maintenance (CBM) and Predictive Maintenance (PdM) for problem occurrence, semiconductor manufacturing equipment uses equipment raw data. This equipment raw data is equipment data at short sampling intervals during processing. In the LSI wafer production line, this equipment data is analyzed to thereby diagnose an equipment status and monitor fault occurrence.
In LSI wafer manufacturing, various kinds of semiconductor manufacturing equipment are used. For example, in order to form a device, an oxidized thin film is formed by thermal oxidation equipment, a gate electrode film is deposited by LPCVD (Low Pressure Chemical Vapor Deposition) equipment, a resist pattern is formed by equipment such as exposure equipment, and then a gate electrode is formed by etching equipment. Moreover, in the wiring formation, a dielectric film is deposited by, for example, plasma CVD equipment, a resist pattern is formed, and then a hole and a groove are formed by the etching equipment. Then copper is filled in the hole and the groove by plating equipment and the copper on a wafer surface is removed by CMP (Chemical Mechanical Polishing) equipment. Moreover, depending on required machining performance and machining accuracy, equipment to be used are selected. There are various models for equipment, and there are also a plurality of semiconductor equipment vendors. The LSI wafer is processed by a wide variety of equipment.
Such a wide variety of manufacturing equipment are used in an LSI production plant. For the purpose of improving productivity, facility informatization has been advanced in the plant. The plant and each of the equipment are connected together by a network, and communication is made based on communication standards that are common between the different equipment. Moreover, multiple function addition/informatization as described above have already been advanced. Shown in International SEMATECH Manufacturing Initiative, ISMI Predictive Preventive Maintenance Implementation Guideline, Technology Transfer #10105119A-TR is a method of, for all the semiconductor manufacturing equipment in general, performing the equipment status diagnosis and the fault monitoring by use of the equipment (raw) data. Data items and contents vary depending on the equipment and a process, but the equipment data itself can be analyzed as a signal at short sampling intervals by a common method.
FIG. 1 shows configuration of plasma etching equipment as an example of the semiconductor manufacturing equipment. In FIG. 1, the etching equipment 101 is composed of: a chamber 102, an electrode 103, a wafer 105, an electrode 106, an exhaust system 107, a gas supply system 108, an equipment controller-outside communication equipment 109, an OES (Optical Emission Spectrometry) 110, a calculator-storage equipment 111 as a calculator system, a screen-user interface 112 as a terminal, flow rate adjustment equipment 113, pressure adjustment equipment 114, power adjustment equipment 115, and temperature adjustment equipment 116. The chamber 102 is provided with a window 121, and light 122 provided by plasma can be observed by the OES 110.
The etching equipment 101 is connected to an equipment data DB 132 via a network 131, and also equipment data monitoring equipment 133 as a calculator system which achieves convenience of data sharing and which monitors and analyzes equipment data of a plurality of semiconductor manufacturing equipment is also connected to the network 131. Needless to say, the equipment data monitoring equipment 133 may be included inside the semiconductor manufacturing equipment 101, in which case the calculator-storage equipment 111 performs processing.
The etching equipment 101 includes the flow rate adjustment equipment 113, the pressure adjustment equipment 114, the power adjustment equipment 115, and the temperature adjustment equipment 116 as actuators, which can adjust flow rates of various gas materials, pressure inside the chamber 102, current and voltage applied to the electrodes 103 and 106, and temperature, respectively. These adjustments are executed based on instructions of the equipment controller-outside communication equipment 109. Pieces of data obtained by monitoring driving signals of these adjustments serve as pieces of equipment data. These pieces of equipment data are signals of the adjustment equipment that operate based on values previously instructed for each time point (processing step), and thus basically become signals with constant values between the time points although noise is put in the signals. There is no correlation between the plurality of signals.
A plasma 104 is involved in light emission, and a wavelength and intensity of this light depend on presence of ionized and dissociated atoms and molecules in the plasma and presence of a substance generated through etching response. Thus, for this light 122, light emission intensity is monitored by the OES 110 on an individual wavelength basis. OES data is data obtained by observing process response but data sampled at short time intervals, and is thus treated as equipment data. Since this data is a signal indicating chemical response in etching processing, that is, an increase and a decrease in the reacting substance, a signal value varies. There is correlation between the plurality of signals.
FIGS. 2A to 2D show examples of equipment data. FIGS. 2A, 2B, 2C, and 2D show four signals shown in a legend 200, where the signal 1 is a faulty signal and the signals 2, 3, and 4 are signals substantially identical to each other. In the signal 1 203 in FIG. 2A, pulsation is put. In the signal 1 213 of FIG. 2B, hunting occurs. In the signal 1 223 of FIG. 2C, an intensity increase is delayed at a time axis. The signal 1 233 of FIG. 2D is shifted. There is correlation between waveforms in FIGS. 2A and 2C, but presence and absence of pulsation of FIG. 2A cannot be detected based on correlation. Although there is no correlation between waveforms in FIGS. 2B and 2D, noise needs to be detected in FIG. 2B and signal intensity difference needs to be detected in FIG. 2D. Moreover, in a signal obtained by actually monitoring the equipment, noise (variation) such as white noise is included. Accordingly, between the plurality of signals obtained by monitoring the equipment, there are various relationships related to changes, such as whether or not there is correlation, where or not there is variation and whether the variation is large or small, and whether or not there is signal intensity difference.
There are various characteristic signal change patterns (waveform patterns), and thus they are not limited to those shown in FIGS. 2A to 2D, but by detecting such signal change, fault occurrence needs to be judged to take measures against equipment problems and perform maintenance and also preparatory planning processing such as pre-processing and post-processing for preventing problem occurrence needs to be carried out. Moreover, appearing waveform patterns are various, and it is also not necessarily possible to specify beforehand what waveform pattern occurs.
Described in Japanese Patent Application Laid-Open Publication No. 2009-70071 are mainly a threshold setting method with good accuracy in fault detection and a method of obtaining statistic. Described are reasons why principal component analysis PCA using time-series correlation of each item and partial least square PLS are used and fault diagnosis is performed by performing signal processing such as Fourier transformation and wavelet transformation.
Described in Japanese Patent Application Laid-Open Publication No. 2009-147183 is that, with a target put on etching equipment as semiconductor manufacturing equipment, a signal is divided into a baseline component (low-frequency component) and a high-frequency component by short-time Fourier transformation and noise occurrence in particular is detected.
Described in Japanese Patent Application Laid-Open Publication No. 2011-59790 is a method of setting a threshold in design-based and case-based fault detection. Shown are methods of converting signal data into a space of feature amount for the purpose of fault detection, and listed as these methods are: the principal component analysis, independent component analysis ICA, non-negative matrix factorization NMF, projection to latent structure PLS, and canonical correlation analysis CCA. In any case, times-series correlation between signal items or independence relationship between the signal items are analyzed and put into feature amounts. Note that this independence relationship means that there is no correlation.
Described in Japanese Patent Application Laid-Open Publication No. 2004-20193 is a method of dividing a signal into different time zones and performing Fourier transformation and performing the principal component analysis on a spectrum on an individual time zone basis to judge a fault of facility based on a principal component score. This signal is vibration data, acoustic data.
Described in Japanese Patent Application Laid-Open Publication No. 2010-219263 is a method of, with a target put on a plurality of OES signals (waveforms), dividing an OES signal by using time-series correlation to obtain a representative waveform pattern. Also shown is a method of identifying a signal without any change.
The invention relates to a method of, in semiconductor manufacturing equipment capable of monitoring equipment data (signal) at short sampling intervals during manufacturing processing, analyzing the equipment data to thereby monitor fault occurrence in the equipment. The equipment data to be monitored include: those (for example, flow rate, pressure, current, voltage, and temperature) which have no correlation between a plurality of signals; and those (for example, OES data with a change in a signal value during chemical response) which have correlation between a plurality of signals. The plurality of signals to be analyzed include: a plurality of signals with different signal items; and a plurality of signals with the same signal items from the past to the present in repeated process processing. As examples of a waveform pattern expressing a signal fault, there are: noise such as the pulsation (FIG. 2A) and the hunting (FIG. 2B); and changes such as the delay (FIG. 2C) and the shift (FIG. 2D), but they are not limited to those, and thus an unexpected waveform pattern that cannot be predicted beforehand is also included. Thus, it is an object to analyze a plurality of actually sampled signals regardless of whether or not there is correlation between the signals in time series to express difference between the signals.
Described in Japanese Patent Application Laid-Open Publication No. 2009-70071 is that, as signal processing for fault detection, processing using correlation and also processing of acquiring a frequency component are used. This does not make it possible to detect difference between signals also having no frequency component since there is no correlation such as, for example, the shift in FIG. 2D.
In Japanese Patent Application Laid-Open Publication No. 2009-147183, regardless of whether or not there is correlation, a fault cannot be detected based on intensity change in time series in a sampled signal.
The various kinds of signal data transformation methods listed in Japanese Patent Application Laid-Open Publication No. 2011-59790 are basically based on correlation between signals. The independence component analysis ICA is described as a method of breaking down a signal into a sum of signals that are not white noise, and the Non-Negative Matrix Factorization NMF is described as a method of breaking down a signal into a product of a non-negative matrix. These processing are used in acoustic signal processing and image signal processing, and are analysis methods of extracting characteristics from data having noise mixed in the signal. Thus, they are not methods of analyzing difference between a plurality of signal changes, which is shown in, for example, FIG. 2A to 2D.
Japanese Patent Application Laid-Open Publication No. 2004-20193 is limited to processing on a signal having a frequency component.
Japanese Patent Application Laid-Open Publication No. 2010-219263 is limited to data having correlation. Moreover, for identification of a signal without any change, difference between a plurality of signals is not analyzed, and thus identification of a signal as shown in FIG. 2D cannot be performed.
It is an object of the present invention to express difference between a targeted plurality of signals regardless of whether or not there is correlation between the signals in time-series and also without previously assuming a waveform pattern. According to an aspect of the invention, it is possible to detect a fault with a signal change indicating any waveform pattern fault. Moreover, unlike a conventional method based on detection of correlation between signals, it is possible to detect slight change difference between the signals without obtaining correlation. Since the difference can be expressed by using only the obtained plurality of signals, previous parameter setting and waveform pattern setting are not required and its usage is also made easier.